Prediction on Air-Nasal Mucus Partition Coefficients of Odor Compounds

The air-nasal mucus partition coefficient is a crucial property among all of the interaction mechanisms between odor molecules and olfactory receptors, since this property contributes to our sense of smell. Due to the complexity of the mucus composition, in vivo determination of the air-mucus partition coefficient is a technical challenge. A predictable model of the air-mucus partition coefficient can provide valuable insights into the chemical properties that govern olfactory perception and can help design desired odorants. In this study, we propose a novel model based on the deep-layer neural network (DNN) algorithm to predict the air-mucus partition coefficients for a range of odor compounds. The molecular surface charge density (σ-profile) calculated from the COnductor like Screening MOdel for Real Solvents (COSMO-RS) thermodynamic package was adapted as descriptors of structural features of odor molecules. The results revealed that the air-mucus partition coefficients are highly correlated to the σ-profile of the studied compounds. The information obtained from the study provided interpretable results, which not only help in identifying the molecular features that contribute to the air-mucus partition coefficient of odorants but also aid in the design of compounds with the desired odor properties.